Lactate Correlation Paper: Nanostring Analysis of All Data

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Section 1: Expression analysis and model generation using original 10 samples.

Figure 1: Barplot of correlation between biomarker expression and lactate levels
Figure 2: Heatmap of scaled biomarker expression data
Figure 3: Correlation between mean scaled biomarker expression and lactate
Figure 4: Multiple linear regression predictive model
Figure 5: Checking model conditions
Figure 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the predictive model.

Figure 7: Incorporating the additional samples into the established model
Figure 8: Correlation between the models predicted lactate levels for all samples and donor metrics
Figure 9: Expression levels of all biomarkers with lactate for all samples
Figure 10: Heatmap of scaled biomarker expression data for all samples
Figure 11: Correlation between mean scaled biomarker expression and lactate for all samples
Figure 12: PCA using scaled biomarker data for all samples
Figure 13: PCA using scaled biomarker data for the original 10 samples only
Figure 14: Multiple Linear Regression Predictive Model generated using all samples
Figure 15: Checking the new model conditions
Figure 16: Correlation between the new models predicted lactate levels for all samples and donor metrics

Section 1: Expression analysis and model generation using nanostring data.

Expression levels of putative biomarkers identified with RNAseq were validated using a custom nanstring panel and tested for conserved correlation with 3 hour lactate levels. Genes that met the biomarker criteria in both analyses were then used to generate a multiple linear regression predictive model.

Figure 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.

Figure 2: Heatmap with biomarker expression data scaled by gene/row and hierarchally clustered by sample/column. Functional assessment based on the 3 hour lactate levels is labeled above the columns. Data for the 7 biomarkers with sufficient expression and correlation with lactate were used from all 10 of the original liver samples.

Mean relative expression of the geneset correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Figure 3: Mean relative expression of the geneset correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

## 
## Call:
## lm(formula = LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + GSTA1 + 
##     GSTA2, data = mlm.data)
## 
## Residuals:
##        FV2        LV2        LV3        FV3        LV1        FN1        FN2 
## -1.7772858 -0.4726029 -0.6974732 -0.3509557  2.7526045  0.8175029 -0.3654356 
##        LN1        FN3        LN3 
## -0.0006994 -0.0181005  0.1124458 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.1919727  2.8772531  -1.457    0.282
## EPHX1        0.0007600  0.0016188   0.469    0.685
## TKT          0.0245085  0.0283383   0.865    0.478
## GPX2        -0.0013174  0.0018790  -0.701    0.556
## JUN          0.0082264  0.0293291   0.280    0.805
## CYP2B6      -0.0004416  0.0004110  -1.074    0.395
## GSTA1       -0.0001505  0.0001989  -0.757    0.528
## GSTA2        0.0013193  0.0010763   1.226    0.345
## 
## Residual standard error: 2.488 on 2 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.8926 
## F-statistic: 11.68 on 7 and 2 DF,  p-value: 0.0811

Figure 4: Generating a Multiple Linear Regression Predictive Model and applying it to the observed expression levels. The model is generated by incorporating expression data from the 7 biomarker genes across the 10 original sample livers and calculating a functional relationship to the lactate levels observed in their corresponding sample. The resulting intercept and coefficients for individual gene expression levels can be used to predict lactate levels for a sample. The plot displays the relationship between the levels predicted by the model when gene expression levels observed in the original 10 sample livers are used and the actual observed levels for those livers. The dashed line represents a 1:1 relationship between obersved and predicted lactate levels.

Checking model conditions. These figures are used to check the quality of the model by testing specific statistical parameters that are required for reliable application.

Figure 5: Checking model conditions. These figures are used to check the quality of the model by testing specific statistical parameters that are required for reliable application.

Figure 6: The predicted lactate levels generated by the model were tested for correlation with the known donor metrics.

Section 2: Incorporating additional samples into the model.

In order to validate the capabilities of these genes to serve as biomarkers, expression was measured in 7 additional livers with the custom nanostring panel.

Figure 7: Incorporating the additional samples into the established model

Figure 8: Correlation between the models predicted lactate levels and donor metrics

Figure 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels. The black lines represent 80% correlation and the red represents 60% correlation

Figure 10: Heatmap with data scaled by gene/row and clustered by sample/column. Functional data and DCD/DBD status is labeled above the columns

Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Figure 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Figure 12: PCA using scaled data for all 17 samples

Figure 13: PCA using scaled data for the original 10 samples only

## 
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + 
##     GSTA1 + GSTA2, data = mlm.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4583 -2.4911 -0.7999  1.5243  9.4388 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -1.080e+00  3.165e+00  -0.341   0.7406  
## EPHX1       -1.944e-03  1.240e-03  -1.568   0.1513  
## TKT          3.048e-02  1.193e-02   2.555   0.0309 *
## GPX2        -2.012e-05  1.240e-03  -0.016   0.9874  
## JUN          9.958e-04  3.370e-03   0.295   0.7743  
## CYP2B6       7.556e-05  3.593e-04   0.210   0.8381  
## GSTA1        1.466e-04  2.032e-04   0.721   0.4890  
## GSTA2        1.223e-03  7.761e-04   1.576   0.1496  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.661 on 9 degrees of freedom
## Multiple R-squared:  0.7791, Adjusted R-squared:  0.6073 
## F-statistic: 4.535 on 7 and 9 DF,  p-value: 0.01976

Figure 14: Generating a Multiple Linear Regression Predictive Model using all samples and applying it to the observed expression levels of all 17 samples

Figure 15: Checking the new model conditions

Figure 16: Correlation between the new models predicted lactate levels and donor metrics

Lactate Correlation Paper: Nanostring Analysis of All Data

John Santiago

2022-03-23

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Table of Contents

Section 1: Expression analysis and model generation using original 10 samples.

Figure 1: Barplot of correlation between biomarker expression and lactate levels

Figure 2: Heatmap of scaled biomarker expression data

Figure 3: Correlation between mean scaled biomarker expression and lactate

Figure 4: Multiple linear regression predictive model

Figure 5: Checking model conditions

Figure 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the predictive model.

Figure 7: Incorporating the additional samples into the established model

Figure 8: Correlation between the models predicted lactate levels for all samples and donor metrics

Figure 9: Expression levels of all biomarkers with lactate for all samples

Figure 10: Heatmap of scaled biomarker expression data for all samples

Figure 11: Correlation between mean scaled biomarker expression and lactate for all samples

Figure 12: PCA using scaled biomarker data for all samples

Figure 13: PCA using scaled biomarker data for the original 10 samples only

Figure 14: Multiple Linear Regression Predictive Model generated using all samples

Figure 15: Checking the new model conditions

Figure 16: Correlation between the new models predicted lactate levels for all samples and donor metrics

Section 1: Expression analysis and model generation using nanostring data.

Section 2: Incorporating additional samples into the model.

In order to validate the capabilities of these genes to serve as biomarkers, expression was measured in 7 additional livers with the custom nanostring panel.